Leveraging big data and machine learning for real-time automotive braking performance analysis

This study proposes a real-time automobile braking performance analysis framework based on big data and machine learning technology, aiming to improve driving safety and the intelligent level of vehicle performance evaluation. By designing an efficient data preprocessing process, including sliding average filtering and linear interpolation methods, combined with the long short-term memory network (LSTM) model, accurate prediction of braking distance and response time is achieved, with a mean square error as low as 0.25, which is better than the traditional model. At the same time, the system architecture adopts a distributed computing framework to ensure millisecond-level real-time reasoning capabilities. Experimental results show that a reasonable setting of the discard rate can effectively improve the generalization ability of the model, verify the applicability of the framework in complex driving scenarios, and provide technical support for the development of intelligent driving systems.